Optimizing Lexical search for large corpora depends on how efficiently text data, indexes, and metadata are stored. The primary storage strategy is to use inverted indexes—data structures that map each term to the list of documents in which it appears. These indexes are compact, fast to traverse, and allow efficient Boolean operations like AND and OR for query matching. However, as corpus size grows, developers must manage the balance between index granularity and storage overhead. For instance, storing positional information (term positions within a document) improves phrase matching but increases disk usage. Compressing posting lists with methods like variable-byte encoding or Golomb coding can reduce storage size without significantly slowing query performance.
Another optimization strategy involves sharding and partitioning large indexes. Splitting data across shards—based on document IDs, time ranges, or semantic topics—allows parallel query processing and better memory utilization. Developers can also use caching for high-frequency terms to reduce repetitive lookups and leverage columnar storage for metadata, which accelerates filtering operations. Periodic index merging (also called segment consolidation) keeps storage compact and query latency predictable, as too many fragmented indexes can degrade performance.
When Lexical search coexists with a vector database such as Milvus, efficient storage strategies extend to managing both symbolic and semantic data. Developers can keep text and embeddings synchronized through shared document IDs, while storing embeddings in Milvus and Lexical indexes in a separate store. This architecture allows Milvus to handle dense numerical data efficiently while Lexical indexes handle sparse text lookups. The combination enables fast candidate retrieval through Lexical filtering and deeper contextual ranking through vector similarity—without overwhelming either system’s storage capacity.